Chronos: Learning the Language of Time Series with Abdul Fatir Ansari - 685

Today we're joined by Abdul Fatir Ansari, a machine learning scientist at AWS AI Labs in Berlin, to discuss his paper, "Chronos: Learning the Language of Time Series" - arxiv.org/abs/2403.07815. Fatir explains the challenges of leveraging pre-trained language models for time series forecasting. We explore the advantages of Chronos over statistical models, as well as its promising results in zero-shot forecasting benchmarks. Finally, we address critiques of Chronos, the ongoing research to improve synthetic data quality, and the potential for integrating Chronos into production systems.
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📖 CHAPTERS
===============================
00:00 - Introduction
02:11 - Inspiration for Chronos
04:30 - Overview of statistical models
07:04 - Overfitting
08:17 - LLMs in time series forecasting
10:20 - Tokenization
15:25 - Why T5?
16:35 - Data augmentation
25:28 - Evaluation
27:45 - Result
31:15 - In domain vs zero shot
33:35 - Performance across different patterns
36:25 - Critique of Chronos
40:15 - Chronos in production
41:00 - Future of Chronos
42:00 - Conclusion
🔗 LINKS & RESOURCES
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Large Language Models Are Zero-Shot Time Series Forecasters - arxiv.org/pdf/2310.07820
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models - arxiv.org/abs/2310.01728
LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters - arxiv.org/pdf/2308.08469
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
- arxiv.org/abs/2310.08278
Unified Training of Universal Time Series Forecasting Transformers (Moirai) - arxiv.org/abs/2402.02592
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[@syedmohammadghazi6133]

Hey, that's a good work...
I've done my research on similar lines..gonna publish it end year.
Just wanted to know have you heard about TranAD models? Although it's basically for anomaly detection, but just curious how better would it be for your use case

[@btcoal]

Paper link?

[@twimlai]

Hi @btcoal. Here's the paper link: arxiv.org/abs/2403.07815.